Service prediction model training method and device for protecting data privacy

A prediction model and data protection technology, applied in the field of privacy protection to achieve the effect of protecting privacy data

Active Publication Date: 2022-05-17
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, data samples owned by different companies or institutions usually contain a large amount of private data, once the information is leaked, it will lead to irreparable negative effects

Method used

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  • Service prediction model training method and device for protecting data privacy
  • Service prediction model training method and device for protecting data privacy
  • Service prediction model training method and device for protecting data privacy

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Experimental program
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Embodiment Construction

[0082] The solutions provided in this specification will be described below in conjunction with the accompanying drawings.

[0083] Picture 1-1 It is a schematic diagram of an implementation structure of an embodiment disclosed in this specification. Wherein, the server communicates with multiple member devices respectively, and can perform data transmission. The number N of multiple member devices may be 2 or a natural number greater than 2. The communication connection can be through a local area network or through a public network. Each member device can have its own business data. Multiple member devices jointly train the business prediction model through data interaction with the server. The business prediction model trained in this way uses the business data of all member devices as data samples. The performance and robustness of the trained model will also be better.

[0084] The client-server architecture composed of the above server and more than two member device...

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Abstract

The embodiment of this specification provides a service prediction model training method and device for protecting data privacy. During the training process, member devices use the object feature data held by themselves to make predictions through the service prediction model, and use the prediction results to determine the update parameters used to update the model parameters, including multiple child parameter; using multiple sub-parameters, multiple computing layers are divided into the first type of computing layer and the second type of computing layer, the sub-parameter values ​​of the first type of computing layer are within the specified range; the sub-parameters of the first type of computing layer are Privacy processing, and output processed sub-parameters. The processed sub-parameters of multiple member devices may be aggregated into an aggregated sub-parameter. The member devices can obtain the aggregated sub-parameters of the first type of computing layer, and use the aggregated sub-parameters and the sub-parameters of the second type of computing layer to update the model parameters.

Description

technical field [0001] One or more embodiments of this specification relate to the technical field of privacy protection, and in particular to a method and device for training a business prediction model that protects data privacy. Background technique [0002] With the development of artificial intelligence technology, neural networks have been gradually applied in areas such as risk assessment, speech recognition, face recognition and natural language processing. The neural network structure in different application scenarios has been relatively fixed. In order to achieve better model performance, more training data is needed. In fields such as medical care and finance, different companies or institutions have different data samples. Once these data are jointly trained, the accuracy of the model will be greatly improved. However, data samples owned by different companies or institutions usually contain a large amount of private data, once the information is leaked, it wil...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/04G06N3/08G06Q10/04G06F21/62
CPCG06N3/084G06Q10/04G06F21/6245G06N3/045
Inventor 郑龙飞陈超超王力张本宇
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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